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Tracking Strains in the Microbiome: Insights from Metagenomics and Models

Ilana L Brito1, Eric J Alm1

  • 1Department of Biological Engineering, Massachusetts Institute of TechnologyCambridge, MA, USA; Center for Microbiome, Informatics and Therapeutics, Massachusetts Institute of TechnologyCambridge, MA, USA.

Frontiers in Microbiology
|June 1, 2016
PubMed

Insights

Commensal microbes, vital for health, are transmitted between people and environments. New genomic techniques track these microbes at the strain level, revealing transmission patterns and mechanisms.

Area of Science:

  • Microbiology
  • Genomics
  • Human Health

Background:

  • Commensal microbes, essential for human health, are increasingly recognized for their roles beyond pathogenic organisms.
  • The transmission dynamics and health impacts of commensal microbes remain understudied, despite their wide-ranging effects, from preventing autoimmune disorders to spreading antibiotic resistance genes.
  • Key questions persist regarding the proportion of the microbiome that is transmissible, primary transmission routes, and the most frequently transmitted organisms.

Purpose of the Study:

  • To highlight advanced techniques for analyzing strain-level resolution of microbes from metagenomic data.
  • To review recent studies tracking microbial strains across human populations and diverse environments.
  • To propose the application of pathogenic transmission models to understand commensal microbial movement.

Main Methods:

  • Utilizing high-resolution genomic data, specifically shotgun metagenomics, to achieve strain-level resolution of microbial organisms.
  • Applying computational advances to analyze raw metagenomic reads for accurate microbial identification and tracking.
  • Leveraging epidemiological models typically used for pathogen transmission to study commensal microbe movement.

Main Results:

  • Strain-level resolution from metagenomic data enables precise tracking of microbial transmission events.
  • Recent studies demonstrate the feasibility of following specific microbial strains across individuals and environmental niches.
  • The application of established transmission models offers a framework for understanding commensal microbial ecology.

Conclusions:

  • Advanced genomic and computational methods are crucial for elucidating commensal microbe transmission.
  • Understanding commensal transmission is vital for predicting and managing their health impacts, including disease prevention and antibiotic resistance.
  • Future research should focus on integrating strain-level tracking with epidemiological modeling to fully characterize microbial exchange within and between communities.
Keywords:
bacterial genomicsbiologicalgenotyping techniquesmetagenomicsmicrobiomemodelsstrain diversity

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